Abdullah Al Mehedi
Union City,USA
Summary
ML and Geospatial Data Scientist with 4+ years of experience in predictive modeling for hydrology and climate sciences. Expert in ML/DL models, including physics-informed approaches, time-series forecasting, and explainable AI techniques. Proficient in Python, with experience in frameworks: TensorFlow, PyTorch, and scikit-learn, handling large-scale datasets and geospatial analysis. Skilled in integrating remote sensing data (e.g., SAR, MODIS, Landsat) and GIS tools. Experienced in leveraging HPC clusters and cloud-computing (AWS, GC) for scaling AI workloads and automating workflows. Published in leading journals like Nature and Elsevier, contributing innovative methods in ML/DS.
Overview
9
9
years of professional experience
Work History
Hydrologic Data Scientist
First Street Technology
09.2024 - Current
- Designed and implemented large-scale probabilistic predictive models for flood forecasting across climatic scenarios globally, processing terabyte-scale geospatial datasets.
- Trained ML models for risk analysis and forecasting in flooding using synthetic and real-world datasets.
- Developed end-to-end streamflow predictive solutions leveraging DL architectures (Uni/Bi-LSTM, Encoder-Decoder, GRU) for multivariate time-series analysis of over 15M+ observations, enhancing accuracy by 25%.
- Deployed and optimized ML/DL and physics-based flood models on HPC clusters and AWS EC2, utilizing SLURM job scheduling for high parallelization, reducing runtime by 40%.
- Optimized trade-offs in runtime, cost, and data resolution for scalable model deployment.
- Quantified uncertainties by generating thousands of realizations of hydrologic variables (e.g., streamflow, flood depth), enhancing robustness and reliability of predictive systems.
Postdoctoral Research Associate
New York University
05.2024 - 09.2024
- Developed DL models, including Bayesian Neural Networks, to analyze river flow with stochastic elements, accurately capturing extreme values 95% of the time.
- Applied stochastic differential equations, probabilistic methods, and Bayesian inference to enhance model reliability and predictive accuracy with a simpler parametric space.
- Quantified culvert resiliency with large-scale geospatial data (3-m resolution) using HPC clusters, achieving high computational efficiency.
Research Assistant
Villanova Center of Resilient Water Systems
10.2021 - 04.2024
- Integrated ML/DL and data analytics to address water and socio-economic prediction
- Developed physics-informed MLP, (Uni/Bi) LSTM, GRU, CNN-LSTM models, enhancing prediction accuracy for Green Stormwater Infrastructure
- Created hybrid physics-guided ML/DL classifiers/regressors to improve urban flood risk assessments
- Enhanced flood mapping accuracy in GEE using SAR/optical imagery using ML classifiers
- Developed MultiSenseRF integrating SAR bands, temporal change signals, and landscape features for enhanced flood mapping in urban environments
- Utilized LSTM models to analyze long-term river flow changes (50 years) with synthetic inputs
- Enhanced model interpretability with explainable AI (e.g., feature importance) techniques
- Automated ML/DS pipelines for predictive analysis in hydrologic processes and flooding
ML Scientist and Summer Institute Fellow
National Water Center - Office of Water Prediction
06.2022 - 08.2022
- Developed geospatial ML pipelines to predict river roughness and flood inundation across the US
- Integrated data from the National Hydrography dataset, USGS data, and remote sensing imagery
- Conducted statistical analyses to quantify the spatio-temporal dynamics of channel roughness
- Led investigations, supervised analyses, conducted modelling experiments, authored article
Graduate Teaching Assistant
Brandenburg University of Technology (BTU)
03.2020 - 10.2020
- Conducted training sessions on surface water hydrologic modelling tools for graduate students
- Delivered engaging lectures on numerical modelling schemes (FDM, FVM, FEM)
Engineering Project coordinator
Orion Group
07.2016 - 10.2017
- Supervised and managed designs and on-site engineers
- Created feasibility report and liaised with the decision-making committee
Education
Ph.D. - Urban Hydrology and Machine Learning, Artificial Intelligence, Hydroinformatics, Remote Sensing
Villanova University (VU)
04.2024
MSc - Hydroinformatics and Water Management
Brandenburg University of Technology (BTU)
10.2019
BSc - Civil Engineering
Bangladesh University of Engineering and Technology (BUET)
02.2015
Skills
Python, R, SQL, MATLAB, C++, JavaScript, Unix shell scripting (Bash), Regression, classification, clustering, time series forecasting, image classification, Feature engineering, statistical analysis, inference techniques, scikit-learn, TensorFlow, Keras, PyTorch, SciPy, CARAT, Matplotlib, Plotly, Folium, ggplot2, Shiny, AWS (EC2), GCP, SLURM job scheduling on HPC clusters, ArcGIS Pro, GEE, QGIS, GeoPandas, GDAL, Sentinel, Landsat, MODIS, ChatGPT, GitHub Copilot, Google Gemini, Python-based pipelines for data preprocessing, model execution, output analysis, Differential calculus, numerical discretization (FD, FV, FE)
Diplomas Certifications
- MITx MicroMasters: Statistics and Data Science (4 out of 5 courses completed)
- IBM Data Science: Professional Certificate (10 courses with a comprehensive capstone project)
- DeepLearning.AI: Specialization in Deep Learning (5 courses with capstone projects)
Awards Scholarships
- National Water Center (NWC) Innovators Program Fellow 2022.
- VUCC (Villanova University Career Center) Professional Development Grant 2023.
Timeline
Hydrologic Data Scientist
First Street Technology
09.2024 - Current
Postdoctoral Research Associate
New York University
05.2024 - 09.2024
ML Scientist and Summer Institute Fellow
National Water Center - Office of Water Prediction
06.2022 - 08.2022
Research Assistant
Villanova Center of Resilient Water Systems
10.2021 - 04.2024
Graduate Teaching Assistant
Brandenburg University of Technology (BTU)
03.2020 - 10.2020
Engineering Project coordinator
Orion Group
07.2016 - 10.2017
MSc - Hydroinformatics and Water Management
Brandenburg University of Technology (BTU)
BSc - Civil Engineering
Bangladesh University of Engineering and Technology (BUET)
Ph.D. - Urban Hydrology and Machine Learning, Artificial Intelligence, Hydroinformatics, Remote Sensing
Villanova University (VU)
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